DocumentCode
467731
Title
Solving Multi-Objective Optimization Problems by a Bi-Objective Evolutionary Algorithm
Author
Wang, Yu-Ping
Author_Institution
Xidian Univ., Xi´´an
Volume
2
fYear
2007
fDate
19-22 Aug. 2007
Firstpage
1018
Lastpage
1024
Abstract
In this paper a novel model for multiobjective optimization problem is proposed first, in which the multiobjective optimization problem is transformed into a bi-objective optimization problem. In this bi-objective problem one objective is responsible for optimizing the quality of the solutions, and the other is to improve the distribution of the obtained nondominated solution set. Then a new crossover operator and selection scheme are designed. Based on these, a specific-designed evolutionary algorithm is presented. The simulations on five widely used benchmark problems are made and the results indicate that the proposed algorithm is efficient and outperforms the compared algorithms.
Keywords
evolutionary computation; optimisation; set theory; biobjective evolutionary algorithm; crossover operator; multiobjective optimization problems; nondominated solution set; Computer science; Cybernetics; Distributed computing; Electronic mail; Evolutionary computation; Genetic programming; Machine learning; Mathematical model; Pareto optimization; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-0973-0
Electronic_ISBN
978-1-4244-0973-0
Type
conf
DOI
10.1109/ICMLC.2007.4370292
Filename
4370292
Link To Document